Before electronic commerce, recommendations were made by people who had knowledge of the products being recommended and the potential consumer of the products. For example, the owner of a music store might recommend a particular artist to a customer based on the owner's knowledge of the customer's musical tastes. The value of the recommendation to the customer, however, was dependent on the owner's ability to accurately assess the customer's tastes as well as the owner's depth of knowledge of the music. As such, recommendations were fundamentally based on a subjective, human process.
In the age of electronic commerce, this is no longer feasible. The amount of data available, be it in the form of different musical titles and artists or different products, means that very few people now have knowledge of the entire spectrum of products available. Therefore, recommendation engines have been developed that use a computer to sift the data, correlate known information about products and make recommendations to consumers based the data available to the computer about the consumer.
Recommendation engines, often in the form of software that analyzes available data, are now used to make instant recommendations for all types of products to consumers. For example, after an Internet purchase of a book (or, indeed, any other product) on Amazon.com, the purchaser is instantly given several recommendations automatically generated by the system in the hopes that the purchaser will make an additional purchase. Similarly, in the context of music, any listener of music through an Internet service may now be automatically provided with lists of “recommended” songs or lists of “people who like this song/artist also like these” songs.
Recommendation engines typically are based on either filtering or ranking. Filtering refers to screening a product or group of products to identify those that match one or more predetermined criteria. Ranking refers to creating an algorithm that weights and sums different types of data for products to numerically create a one-dimensional ranked set of products. The accuracy of the recommendation depends on the data and the algorithm or criteria selected to analyze the data.
Recommendation engines, however, tend to rely on subjective, self-inconsistent data provided by a diverse number of sources. Such data include consumer reviews and ratings, subjective consumer classifications (e.g., a genre classification of a song or inclusion in a “best of” list developed by a consumer or critic). Even though statistical methods and additional objective data (e.g., size, price, color, compatibility, or other objective traits) may be employed to improve the ability of a recommendation engine to correlate the similarity between products to be recommended, the quality of recommendations by automatic recommendation engines is still very inconsistent.
One specific point of inconsistency directly impacts a function of recommendation engines that is very important to those who operate them: the ability to objectively determine the similarity of a product to other products. This function is typically referred to as “diversity.” Diversity in recommendations is important because in many situations, the purpose of making a recommendation is to recommend a product or item to a consumer that the consumer is actually interested in but that the consumer would not have sought out absent the recommendation. Thus, the recommendation engine can satisfy a consumer need that would otherwise not have been satisfied or even known.
Diversity is also useful in other ways. For example, in the context of automatically selecting songs to be played to a listener with known tastes (a form of recommendation in which what is recommended is actually consumed) it is known that listeners do not wish to hear the most highly rated songs repeatedly. Thus, after the obvious selections based on direct knowledge of the consumer's taste have been made, diversity is then used to select other songs that the consumer is not familiar with in the hopes that the song will match the consumer's taste. Diversity is also useful in determining when to begin repeating songs to the consumer, the alternative being simply ranking every song for the consumer and playing the songs in order of rank until every song known has been played.